In machine learning and pattern recognition, feature selection has been a hot
topic in the literature. Unsupervised feature selection is challenging due to
the loss of labels which would supply the related information.How to define an
appropriate metric is the key for feature selection. We propose a filter method
for unsupervised feature selection which is based on the Confidence Machine.
Confidence Machine offers an estimation of confidence on a feature'reliability.
In this paper, we provide the math model of Confidence Machine in the context
of feature selection, which maximizes the relevance and minimizes the
redundancy of the selected feature. We compare our method against classic
feature selection methods Laplacian Score, Pearson Correlation and Principal
Component Analysis on benchmark data sets. The experimental results demonstrate
the efficiency and effectiveness of our method.